Three-dimensional scanning and digitization of the surface geometry of objects is commonly used in many industries and services and their applications are numerous. A few examples of such applications are: 3D inspection and measurement of shape conformity in industrial production systems, digitization of clay models for industrial design and styling applications, reverse engineering of existing parts with complex geometry, interactive visualization of objects in multimedia applications, three-dimensional documentation of artwork and artefacts, human body scanning for biometry or better adaptation of orthoses.
The shape of an object is scanned and digitized using a ranging sensor that measures the distance between the sensor and a set of points on the surface. Different principles have been developed for optical range sensors (see F. Blais, “A Review of 20 Years of Range Sensor Development”, in proceedings of SPIE-IS&T Electronic Imaging, SPIE Vol. 5013, 2003, pp. 62-76) that make it possible to capture a dense set of measurements on the object surface. From these measurements, three dimensional coordinates of points on the target surface are obtained in the sensor reference frame. From a given viewpoint, the ranging sensor can only acquire distance measurements on the visible portion of the surface. To digitize the whole object, the sensor is therefore moved to a plurality of viewpoints in order to acquire sets of range measurements that cover the entire surface. A model of the object's surface geometry can be built from the whole set of range measurements provided in the same global coordinate system.
While acquiring the measurements, the sensor can be moved around the object using a mechanical system or can be hand-held for more versatility. Portable hand-held systems are especially useful for rapid scanning and for objects that are scanned on site. Using a hand-held system, the main challenge is to continuously estimate the position and orientation (6 degrees of freedom) of the apparatus in a global coordinate system fixed relative to the object. This can be accomplished using a positioning device coupled to the range scanner. The positioning device can be electromagnetic (see for example products by Polhemus), mechanical (see for example products by Faro), optical (see for example products by Steinbichler) or ultrasonic (see Arsenault et al., “Portable apparatus for 3-dimensional scanning”, U.S. Pat. No. 6,508,403 B2, Jan. 21, 2003). Using a positioning device significantly increases the complexity and cost of the apparatus. It is also cumbersome or, in some cases, noisy enough to limit the quality of the integrated data.
To avoid the usage of an external positioning device, an alternative consists of using the 3D measurements collected on a rigid object in order to compute the relative position and orientation between the apparatus and the object. It is even possible to hold and displace the object in hand while scanning (see S. Rusinkiewicz, O. Hall-Holt and M. Levoy, “Real-Time 3D Model Acquisition”, in ACM Transactions on Graphics, vol. 21, no. 3, July 2002, pp. 438-446, F. Blais, M. and G. Godin, “Accurate 3D Acquisition of Freely Moving Objects,” in proc. of the Second International Symposium on 3D Data Processing, Visualization and Transmission. Thessaloniki, Greece. Sep. 6-9, 2004. NRC 47141). This idea of integrating the computation of the position directly into the system while exploiting measurement is interesting but these systems depend completely on the geometry of the object and it is not possible to ensure that an accurate estimate of the pose be maintained. For example, objects whose geometry varies smoothly or objects with local symmetries including spherical, cylindrical or nearly planar shapes, lead to non constant quality in positioning.
To circumvent this limitation, one can exploit principles of photogrammetry by using fixed points or features that can be re-observed from various viewpoints in the scene. These positioning features can be natural points in the scene but in many cases their density or quality is not sufficient and positioning markers are set in the scene. One may thus collect a set of images and model the 3D set of positioning features in a same global coordinate system. One can further combine this principle using a camera with a 3D surface scanner. The complementarity of photogrammetry and range sensing has been exploited (see products by GOM mbH, CogniTens Ltd. and Steinbichler Optotechnik GmbH) where a white light projector is used with cameras that observe the illuminated scene including positioning features (markers). Using this type of system, a photogrammetric model of the set of markers is measured and built beforehand, using a digital camera. Then, the 3D sensor apparatus is displaced at a set of fixed positions to measure the surface geometry. The range images can be registered to the formerly constructed model of positioning features since the 3D sensor apparatus can detect the positioning markers.
An interesting idea is to integrate within the same system, a hand-held scanner projecting a laser light pattern along with the capability of self-referencing while simultaneously observing positioning features. Hebert (see P. Hebert, “A Self-Referenced Hand-Held Range Sensor”. in proc. of the 3rd International Conference on 3D Digital Imaging and Modeling (3DIM 2001), 28 May-1 Jun. 2001, Quebec City, Canada, pp. 5-12) proposed to project laser points on the object to be scanned with an external fixed projector to help position the hand-held sensor. This type of system can be improved by making it capable of building a model of the positioning feature points dynamically. Moreover, the 3D range scanner device was improved to simultaneously capture 3D surface points along with positioning features obtained from retro-reflective markers (see US Patent Publication No. 2008/0201101).
While the sensor projects a laser pattern to recover dense 3D surface points, it also projects light from LED in order to recover a signal on the light detectors, arising from the reflection of light on the retro-reflective markers that are fixed in the observed scene. The system can then simultaneously build a 3D model of these reference markers for positioning while acquiring a dense set of 3D surface measurements.
Nevertheless, there are constraints related to the usage of such a hand-held system. In order to obtain high accuracy for 3D surface measurements, the sensing device must acquire data while being as close as possible to the surface to be scanned. This imposes a reduced field of view on the object and consequently, the distance between retro-reflective markers must be reduced. For larger objects such as vehicles or architectural structures that exceed the working volume size of one cubed meter, this becomes not optimal when it is necessary to scan the whole object surface or when it is necessary to scan sections of these objects but in a common global coordinate system. Actually, positioning errors accumulate and affect the accuracy of the recovered 3D surface model.